Impact of the COVID‐19 pandemic on the productivity and career prospects of musculoskeletal researchers
Bibliographic record
Abstract
Academic researchers faced a multitude of challenges posed by the COVID-19 pandemic, including widespread shelter-in-place orders, workplace closures, and cessation of in-person meetings and laboratory activities. The extent to which these challenges impacted musculoskeletal researchers, specifically, is unknown. We developed an anonymous web-based survey to determine the pandemic's impact on research productivity and career prospects among musculoskeletal research trainees and faculty. There were 116 musculoskeletal (MSK) researchers with varying demographic backgrounds who completed the survey. Of respondents, 48.3% (n = 56) believed that musculoskeletal funding opportunities decreased because of COVID-19, with faculty members more likely to hold this belief compared to nonfaculty researchers (p = 0.008). Amongst MSK researchers, 88.8% (n = 103) reported research activity was limited by COVID-19, and 92.2% (n = 107) of researchers reported their research was not able to be refocused on COVID-19-related topics, with basic science researchers less likely to be able to refocus their research compared to clinical researchers (p = 0.030). Additionally, 47.4% (n = 55) reported a decrease in manuscript submissions since the onset of the pandemic. Amongst 51 trainee researchers, 62.8% (n = 32) reported a decrease in job satisfaction directly attributable to the COVID-19 pandemic. In summary, study findings indicated that MSK researchers struggled to overcome challenges imposed by the pandemic, reporting declines in funding opportunities, research productivity, and manuscript submission. Trainee researchers experienced significant disruptions to critical research activities and worsening job satisfaction. Our findings motivate future efforts to support trainees in developing their careers and target the recovery of MSK research from the pandemic stall.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".